On the court, Ty Lawson's gifts are easy to see — the speed, the quickness, the ball handling that help make him an elite point guard in the NBA.

On a computer screen full of numbers, his game looks something like this: PIE (11.8 percent), TS% (54.9), TmTOV% (14.6), ASTRatio (28.2) and NetRtg (4.4).

Confused? You're not alone.

But the people who study it, invented parts of it and are paid to crunch it see advanced metrics such as these in their sleep. And those figures can reveal scary truths such as this: In last season's playoffs, though Lawson was the Nuggets' best player, Corey Brewer had a nearly identical "usage" rate. Meaning, Brewer was nearly as integral to the offense, in terms of possessions per 40 minutes, as was Lawson — though he wasn't close to Lawson in efficiency. Brewer's player impact estimate (PIE) was a paltry 1.5 percent compared with Lawson's 13.9 percent. His player efficiency rate (PER) was 7.35, compared with Lawson's 20.47. The league average is 15.00.

In other words, the ball should have found someone else a lot more often in that playoff series.

That example is just the smallest of nibbles into the NBA's analytics pie. It's a pie the Nuggets are finally taking a full bite out of under the leadership of first-year general manager Tim Connelly. The Nuggets want to be more efficient on the court — and make better personnel decisions off the court.

"I think it's a tool that can't be ignored," Connelly said. "You can't bury your head in the sand and not get better. And it's such a fluid environment, we have to always be up to date with any tools that can help us make better decisions. I've said it all the time: I just want to make 'informed mistakes.' Analytics is part of that."

The Nuggets hired a full-time manager of analytics, Tommy Balcetis. And, yes, Balcetis has the background you think he would: Harvard educated (economics and psychology) and worked as a Fidelity business consultant. He then worked for the NBA in international media, and at a basketball camp in Moscow, he first met Connelly.

But here's the most important point: Balcetis played basketball in his native country, Lithuania, as well as in the United States. He was set to play at Harvard — about the same time as Jeremy Lin, now a Houston Rockets point guard — but a heart condition cut short his career as a player.

Balcetis is among three individuals in the Denver organization who have strong analytic minds. Assistant general manager Arturas Karnisovas came from Houston, which has been one of the NBA leaders in using advanced metrics. And international scout Rafal Juc also has a reputation for digging deep into stats.

"We'll be aggressive in making sure, prior to making any moves or identifying potential acquisitions, that we understand their statistical DNA and we're not left in the dark," Connelly said.

First-year Nuggets coach Brian Shaw is on board. He was old school as an NBA player, but he is eager to keep up with new trends.

"If analytics are part of basketball, then I'm an analytics guy because that's part of the game," Shaw said. "I think everybody around the world knows that I'm not the most technical, stat-driven type of guy, but I understand that it's part of the game and I have embraced it. I don't want to get left behind in the movement."

In this Nuggets front office, he won't.

And, Balcetis said, the numbers won't drown out the game. Basketball has enough intangibles that numbers alone are only part of the puzzle.

"If you want to be a successful analytics person, you need to understand the game, because numbers don't tell everything," Balcetis said. "You look at the numbers, and context is everything when it comes to analytics. At the end of the day, look at who wins championships: teams with franchise players. So, you need to put the numbers in context."

That context can work both ways, helping certain players and hurting others. Former Colorado star Andre Roberson was widely considered to be a second-round pick, at best, before the NBA draft June 27 after turning pro following his junior season with the Buffaloes. That is, until analytics shed a new light on his value. Roberson became a first-round pick. Oklahoma City, a team that believes in advanced metrics, used those numbers to make the decision to acquire him.

The Nuggets will be using more analytics than ever when it comes to draft and free-agent decisions.

"Basketball used to be more based on gut instinct," Balcetis said. "You would have your scouts looking at guys and gauging. You had to be an expert in certain areas to understand whether a guy could play or not play.

"Analytics, it's part of overall decision-making. It's never going to be the No. 1 thing to determine whether a person is a good player or not, but it's going to be a part of a bigger sort of pie in terms of decision-making."

Understanding all of the new statistics and metrics might take some time, but these are the definitions of those that appeared in the story to get you started:

PIE: Player impact estimate measures a player's overall statistical contribution against the total statistics in games in which he plays. PIE is calculated based on player and game statistics only when the player is on the floor.

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